AI Chatbots Remain Overconfident — Even When They’re Wrong: Large Language Models appear to be unaware of their own mistakes, prompting concerns about common uses for AI chatbots.

Interesting talk but the number of times he completely dismisses the entire field of linguistics kind of makes me think he's being disingenuous about his familiarity with it.

For one, I think he is dismissing holotes, the concept of "wholeness." That when you cut something apart to it's individual parts, you lose something about the bigger picture. This deconstruction of language misses the larger picture of the human body as a whole, and how every part of us, from our assemblage of organs down to our DNA, impact how we interact with and understand the world. He may have a great definition of understanding but it still sounds (to me) like it's potentially missing aspects of human/animal biologically based understanding.

For example, I have cancer, and about six months before I was diagnosed, I had begun to get more chronically depressed than usual. I felt hopeless and I didn't know why. Surprisingly, that's actually a symptom of my cancer. What understanding did I have that changed how I felt inside and how I understood the things around me? Suddenly I felt different about words and ideas, but nothing had changed externally, something had change internally. The connections in my neural network had adjusted, the feelings and associations with words and ideas was different, but I hadn't done anything to make that adjustment. No learning or understanding had happened. I had a mutation in my DNA that made that adjustment for me.

Further, I think he's deeply misunderstanding (possibly intentionally?) what linguists like Chomsky are saying when they say humans are born with language. They mean that we are born with a genetic blueprint to understand language. Just like animals are born with a genetic blueprint to do things they were never trained to do. Many animals are born and almost immediately stand up to walk. This is the same principle. There are innate biologically ingrained understandings that help us along the path to understanding. It does not mean we are born understanding language as much as we are born with the building blocks of understanding the physical world in which we exist.

Anyway, interesting talk, but I immediately am skeptical of anyone who wholly dismisses an entire field of thought so casually.

For what it's worth, I didn't downvote you and I'm sorry people are doing so.

I watched this entire video just so that I could have an informed opinion. First off, this feels like two very separate talks:

The first part is a decent breakdown of how artificial neural networks process information and store relational data about that information in a vast matrix of numerical weights that can later be used to perform some task. In the case of computer vision, those weights can be used to recognize objects in a picture or video streams, such as whether something is a hotdog or not.

As a side note, if you look up Hinton’s 2024 Nobel Peace Prize in Physics, you’ll see that he won based on his work on the foundations of these neural networks and specifically, their training. He’s definitely an expert on the nuts and bolts about how neural networks work and how to train them.

He then goes into linguistics and how language can be encoded in these neural networks, which is how large language models (LLMs) work… by breaking down words and phrases into tokens and then using the weights in these neural networks to encode how these words relate to each other. These connections are later used to generate other text output related to the text that is used as input. So far so good.

At that point he points out these foundational building blocks have been used to lead to where we are now, at least in a very general sense. He then has what I consider the pivotal slide of the entire talk, labeled Large Language Models, which you can see at 17:22. In particular he has two questions at the bottom of the slide that are most relevant:

• Are they genuinely intelligent?
• Or are they just a form of glorified auto-complete that uses statistical regularities to pastiche together pieces of text that were created by other people?

The problem is: he never answers these questions. He immediately moves on to his own theory about how language works using an analogy to LEGO bricks, and then completely disregards the work of linguists in understanding language, because what do those idiots know?

At this point he brings up The long term existential threat and I would argue the rest of this talk is now science fiction, because it presupposes that understanding the relationship between words is all that is necessary for AI to become superintelligent and therefore a threat to all of us.

Which goes back to the original problem in my opinion: LLMs are text generation machines. They use neural networks encoded as a matrix of weights that can be used to predict long strings of text based on other text. That’s it. You input some text, and it outputs other text based on that original text.

We know that different parts of the brain have different responsibilities. Some parts are used to generate language, other parts store memories, still other parts are used to make our bodies move or regulate autonomous processes like our heartbeat and blood pressure. Still other bits are used to process images from our eyes and other parts reason about spacial awareness, while others engage in emotional regulation and processing.

Saying that having a model for language means that we’ve built an artificial brain is like saying that because I built a round shape called a wheel means that I invented the modern automobile. It’s a small part of a larger whole, and although neural networks can be used to solve some very difficult problems, they’re only a specific tool that can be used to solve very specific tasks.

Although Geoffrey Hinton is an incredibly smart man who mathematically understands neural networks far better than I ever will, extrapolating that knowledge out to believing that a large language model has any kind of awareness or actual intelligence is absurd. It’s the underpants gnome economic theory, but instead of:

1. Collect underpants
2. ?
3. Profit!

It looks more like:

1. Use neural network training to construct large language models.
2. ?
3. Artificial general intelligence!

If LLMs were true artificial intelligence, then they would be learning at an increasing rate as we give them more capacity, leading to the singularity as their intelligence reaches hockey stick exponential growth. Instead, we’ve been throwing a growing amount resources at these LLMs for increasingly smaller returns. We’ve thrown a few extra tricks into the mix, like “reasoning”, but beyond that, I believe it’s clear that we’re headed towards a local maximum that is far enough away from intelligence that would be truly useful (and represent an actual existential threat), but in actuality only resembles what a human can output well enough to fool human decision makers into trusting them to solve problems that they are incapable of solving.

People really do not like seeing opposing viewpoints, eh? There's disagreeing, and then there's downvoting to oblivion without even engaging in a discussion, haha.

Even if they're probably right, in such murky uncertain waters where we're not experts, one should have at least a little open mind, or live and let live.

Every thread about LLMs has to have some guy like yourself saying how LLMs are like humans and smarter than humans for some reason.

Language models are unsuitable for math problems broadly speaking. We already have good technology solutions for that category of problems. Luckily, you can combine the two - prompt the model to write a program that solves your math problem, then execute it. You're likely to see a lot more success using this approach.

Fun thing, when it gets the answer right, tell it is was wrong and then see it apologize and "correct" itself to give the wrong answer.

Ah, well then, if he tells the bot to not hallucinate and validate output there's no reason to not trust the output. After all, you told the bot not to, and we all know that self regulation works without issue all of the time.

I guess, but it's like proving your phones predictive text has confidence in its suggestions regardless of accuracy. Confidence is not an attribute of a math function, they are attributing intelligence to a predictive model.

The only thing close to a decision that LLMs make is

That's not true. An "if statement" is literally a decision tree.

The only reason they answer questions is because in the training data they’ve been provided

This is technically true for something like GPT-1. But it hasn't been true for the models trained in the last few years.

it knows from its training data that sometimes accusations are followed by language that we interpret as an apology, and sometimes by language that we interpret as pushing back. It regurgitates these apologies without understanding anything, which is why they seem incredibly insincere

It has a large amount of system prompts that alter default behaviour in certain situations. Such as not giving the answer on how to make a bomb. I'm fairly certain there are catches in place to not be overly apologetic to minimize any reputation harm and to reduce potential "liability" issues.

And in that scenario, yes I'm being gaslite because a human told it to.

There is no thinking

Partially agree. There's no "thinking" in sentient or sapient sense. But there is thinking in the academic/literal definition sense.

There are no decisions

Absolutely false. The entire neural network is billions upon billions of decision trees.

The more we anthropomorphize these statistical text generators, ascribing thoughts and feelings and decision making to them, the less we collectively understand what they are

I promise you I know very well what LLMs and other AI systems are. They aren't alive, they do not have human or sapient level of intelligence, and they don't feel. I've actually worked in the AI field for a decade. I've trained countless models. I'm quite familiar with them.

But "gaslighting" is a perfectly fine description of what I explained. The initial conditions were the same and the end result (me knowing the truth and getting irritated about it) were also the same.